Cascaded change detection for foreground segmentation

2007 IEEE Workshop on Motion and Video Computing, WMVC 2007

Volumn , Issue , 2007, Pages

  Teixeira, Luís F ^a   Corte Real, Luís ^a  

^a UNIVERSITY OF PORTO   (Portugal)

Author keywords

[No Author keywords available]

Indexed keywords

COMPUTATIONAL COMPLEXITY; GAUSSIAN DISTRIBUTION; IMAGE SEGMENTATION; MATHEMATICAL MODELS; PIXELS;

CASCADED CHANGE DETECTION; RELEVANT OBJECTS; STRUCTURAL CHANGES;

FEATURE EXTRACTION;

EID: 34547159128     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/WMVC.2007.11     Document Type: Conference Paper

References (14)

1. T. Aach, A. Kaup, and R. Mester. Statistical model-based change detection in moving video. Signal Processing, 31(2): 165-180, March 1993.
2. A. Cavallaro, E. D. Gelasca, and T. Ebrahimi. Objective evaluation of segmentation quality using spatio-temporal context. In Proceedings of IEEE International Conference on Image Processing, September 2002.
3. A. Elgammal, D. Hardwood, and L. Davis. Non-parametric model for background subtraction. In Proceedings of European Conference on Computer Vision, volume 2, pages 751-767, 2000.
4. B. Han, D. Comaniciu, and L. Davis. Sequential kernel density approximation through mode propagation: Applications to background modeling. In Proceedings of Asian Conference on Computer Vision, 2004.
5. I. Haritaoglu, D. Harwood, and L. S. Davis. W4: Real-time surveillance of people and their activities. IEEE Transactions on Pattern Analysis and Machine Intelligence, 22(8):809-830, August 2000.
6. D. Koller, J. Weber, and J. Malik. Robust multiple car tracking with occlusion reasoning. In Proceedings of the European Conference on Computer Vision.
7. D.-S. Lee. Effective gaussian mixture learning for video background subtraction. IEEE Transactions on Pattern Analysis and Machine Intelligence, 27(5): 827-832, May 2005.
8. L. Li, W. Huang, I. Y.-H. Gu, and Q. Tian. Statistical modeling of complex backgrounds for foreground object detection. IEEE Transactions on Image Processing, 13(11): 1459-1472, November 2004.
9. N. Paragios and V. Ramesh. A mrf-based approach for real-time subway monitoring. In Proceedings of IEEE International Conference on Computer Vision and Pattern Recognition, volume 1, pages 1-1034 -1-1040, 2001.
10. M. Piccardi. Background subtraction techniques: a review. In Proceedings of IEEE International Conference on Systems, Man and Cybernetics, October 2004.
11. M. Piccardi and T. Jan. Mean-shift background image modelling. In Proceedings of International Conference on Image Processing, pages 3399-3402, 2004.
12. C. Stauffer and W. Grimson. Adaptive background mixture models for real-time tracking. In Proceedings of IEEE International Conference on Computer Vision and Pattern Recognition, volume 19, pages 246-252, 1999.
13. K. Toyama, J. Krumm, B. Brumitt, and B. Meyers. Wallflower: Principles and practice of background maintenance. In Proceedings of IEEE International Conference on Computer Vision, volume 1, pages 255-261, 1999.
14. C. Wren, A. Azarbayejani, T. Darrell, and A. Pentland. Pfinder: Real-time tracking of the human body. IEEE Transactions on Pattern Analysis and Machine Intelligence, 19(7):780-785, July 1997.